Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: google-bert/bert-base-uncased
+base_model: distilbert/distilbert-base-uncased
 language: en
 license: apache-2.0
 pipeline_tag: text-classification
@@ -28,11 +28,11 @@ inference:
   parameters:
     temperature: 1
 ---
-# 🚀 BERT-based Sentiment Classification Model: Unleashing the Power of Synthetic Data
+# 🚀 (distil)BERT-based Sentiment Classification Model: Unleashing the Power of Synthetic Data
 
 ## Model Details
 - **Model Name:** tabularisai/robust-sentiment-analysis
-- **Base Model:** bert-base-uncased
+- **Base Model:** distilbert/distilbert-base-uncased
 - **Task:** Text Classification (Sentiment Analysis)
 - **Language:** English
 - **Number of Classes:** 5 (*Very Negative, Negative, Neutral, Positive, Very Positive*)
@@ -47,7 +47,7 @@ inference:
 
 ## Model Description
 
-This model is a fine-tuned version of `bert-base-uncased` for sentiment analysis. **Trained only on syntethic data produced by SOTA LLMs: Llama3.1, Gemma2, and more**
+This model is a fine-tuned version of `distilbert/distilbert-base-uncased` for sentiment analysis. **Trained only on syntethic data produced by SOTA LLMs: Llama3.1, Gemma2, and more**
 
 ### Training Data
 
@@ -215,7 +215,7 @@ The model demonstrates strong performance across various sentiment categories. H
 
 ## Training Procedure
 
-The model was fine-tuned on synthetic data using the `bert-base-uncased` architecture. The training process involved:
+The model was fine-tuned on synthetic data using the `distilbert/distilbert-base-uncased` architecture. The training process involved:
 
 - Dataset: Synthetic data designed to cover a wide range of sentiment expressions
 - Training framework: PyTorch Lightning